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Assessment of landslide sensitivity and determination of effective factors in its occurrence using the random forest algorithm (Case study: Glandrood watershed)

by: Journal of Geography and Environmental Hazards

Publication date: 2025-03
Topics: Landslide, Susceptibility Assessment, Random Forest Algorithm, Glandrood Watershed
Collection: journals_contributions; journals
Item Size: 16.4M

The Glandrood watershed, given its geological, tectonic, climatic, hydrological characteristics, topography, and poor vegetation cover, has a landslide potential, and inappropriate human intervention in it leads to the occurrence and intensification of mass movements. In the present study, using a descriptive-analytical and survey approach, an attempt has been made to prepare a sensitivity map for slope instability and landslides in the study area using 11 factors effective in causing slope instability. These factors include: slope, aspect direction, elevation, distance from the road, distance from the fault, distance from the waterway, total annual precipitation, average annual temperature, land use, geology, and slope curvature. Then, a total of 352 landslide points were identified using satellite images and field visits, of which 70% were used for model training and the remaining 30% for validation. Subsequently, the random forest algorithm was coded in the MATLAB R2020a environment to identify areas susceptible to landslides. According to the landslide hazard map in the Glandrood watershed, over 30% of the area is classified as "very high risk," 19% as "high risk," 13% as "medium risk," 19% as "low risk," and 16% of the study area is classified as "very low" landslide risk. The prioritization of effective variables indicates that the highest weight, with a criterion ranking of 0.98, is related to elevation. The analysis of the catena concept, which reflects the relationship between soil patterns and landscape slopes with topography and leads to variability in soil properties and subsequently changes in vegetation cover, can well justify the relationship or influence of the elevation factor on landslide movements in the study area.
Extended Abstract
Introduction
Landslides are one of the most common natural phenomena that generally occur in mountainous regions around the world. In many cases, they lead to financial and human losses and are recognized as a natural disaster. Continuous monitoring of surface changes and identifying areas prone to slope movements, including landslides—especially in human settlements and infrastructure such as roads and railways—are among the most effective factors in reducing the casualties and financial losses from natural hazards like landslides. Many researchers have attempted to present models for hazard zoning of landslide phenomena, or in other words, to create landslide hazard maps, primarily based on inductive methods, quantitative, and statistical modeling. They have examined various factors influencing the occurrence of landslides and then analyzed how these factors affect the distribution of landslides. By correlating the landslide hazard map with land use maps, it is possible to identify areas at risk, including cities, villages, bridges, factories, and other structures, so that necessary measures can be taken to protect these assets.
Materials and Methods
The study area is located in Mazandaran Province, south of Noor County, within the Glandrood watershed, in the central part of the Alborz Mountain range. It is part of the larger Haraz watershed. The Glandrood watershed, given its geological, tectonic, climatic, and hydrological characteristics, topography, and poor vegetation cover, has a landslide potential, and inappropriate human intervention in it leads to the occurrence and intensification of mass movements. In the present study, using a descriptive-analytical and survey approach, an attempt has been made to prepare a sensitivity map for slope instability and landslides in the study area using 11 factors that are effective in causing slope instability. These factors include: slope, aspect direction, elevation, distance from the road, distance from the fault, distance from the waterway, total annual precipitation, average annual temperature, land use, geology, and slope curvature. Then, a total of 352 landslide points were identified using satellite images and field visits, of which 70% were used for model training and the remaining 30% for validation. Subsequently, the random forest algorithm was coded in the MATLAB R2020a environment to identify areas susceptible to landslides.
Results and Discussion
According to the landslide hazard map in the Glandrood watershed, over 30% of the area is classified as "very high risk," 19% as "high risk," 13% as "medium risk," 19% as "low risk," and 16% of the study area is classified as "very low risk" for landslides. The prioritization of effective variables indicates that the highest weight, with a criterion ranking of 0.98, is related to elevation. The analysis of the catena concept, which reflects the relationship between soil patterns and landscape slopes with topography and leads to variability in soil properties and subsequently changes in vegetation cover, can well justify the relationship or influence of the elevation factor on landslide movements in the study area. The study of slope movements in the examined watershed indicates that this area is prone to landslides due to natural conditions such as fault structures, steep slopes, humid climate, and sensitive and non-resistant soil. Human intervention in this area leads to the creation and intensification of these movements.
Conclusion
Identifying landslide-prone areas and evaluating landslide susceptibility is crucial for avoiding these areas and implementing preventive and control methods. One of the main actions in this regard is the preparation of landslide hazard susceptibility maps. Planners and decision-makers can use these maps in various fields such as soil and natural resource conservation management, infrastructure and tourism planning, land allocation for urban and rural development, environmental planning, and determining the routes for roads and power transmission lines. Additionally, determining the impact and importance of each variable on the occurrence of slope movements can be the next step in reducing and controlling these movements in the study area. Modeling using the random forest algorithm based on the variables affecting slope movements in the Glandrood watershed indicates that the largest area of this watershed falls within the classes with very high and high landslide susceptibility. The analysis of the relationship between landslide occurrence and influencing factors shows that elevation is the most significant factor affecting this phenomenon in the study area. To justify the impact of elevation as the most important factor, the relationship between vegetation density and elevation in the Glandrood watershed can be mentioned.